1 Introduction

This paper contains estimates for the effective reproduction number \(R_{t,m}\) over time \(t\) in various countries \(m\) of the world. This is done using the methodology as described in [1]. These have been implemented in R using EpiEstim package [2] which is what is used here. The methodology and assumptions are described in more detail here.

This paper and it’s results should be updated roughly daily and is available online.

As this paper is updated over time this section will summarise significant changes. The code producing this paper is tracked using Git. The Git commit hash for this project at the time of generating this paper was 5ffbff81294c3835c2765bd8086523dfb7f6bf84.

2 Data

Data are downloaded from [3]. Minor formatting is applied to get the data ready for further processing.

3 Basic Exploration

Below cumulative case count is plotted on a log scale by continent.

Cumulative Reported Cases by Continent

Cumulative Reported Cases by Continent

Below a 7-day moving average of daily case count is plotted on a log scale by continent.

Daily Reported Cases by Continent (7-day moving average)

Daily Reported Cases by Continent (7-day moving average)

Below cumulative deaths by country is plotted on a log scale:

Cumulative Reported Deaths by Continent

Cumulative Reported Deaths by Continent

Below a 7-day moving average of daily deaths by country is plotted on a log scale:

Daily Deaths by Continent (7-day moving average)

Daily Deaths by Continent (7-day moving average)

4 Method & Assumptions

The methodology is described in detail here.

Countries with populations of less than 500 000 are excluded.

5 Results

5.1 World-wide

Estimated Type Daily Count (Last Week) Week Ending R - Lower CI R - Mean R - Uppper CI
cases 811,293.7 2021-05-03 1 1 1
deaths 13,290.0 2021-05-03 1 1 1

5.2 Current reproduction number estimates by country

Below current (last weekly) \(R_{t,m}\) estimates are plotted on a world map.

5.2.0.1 Cases

5.2.1 Deaths

5.3 Top 10 countries

Below we show various extremes of \(R_{t,m}\) where counts (deaths or cases) exceed 10 per day in the last week.

5.3.1 Lowest \(R_{t,m}\) based on deaths

Country Estimated Type Daily Count (Last Week) Week Ending R - Lower CI R - Mean R - Uppper CI
Bosnia and Herzegovina deaths 28.7 2021-05-03 0.5 0.5 0.6
Bulgaria deaths 63.9 2021-05-03 0.6 0.7 0.7
United Kingdom deaths 15.6 2021-05-03 0.6 0.7 0.9
South Africa deaths 38.0 2021-05-03 0.7 0.8 0.9
Moldova deaths 15.0 2021-05-03 0.6 0.8 0.9
Ecuador deaths 66.4 2021-05-03 0.7 0.8 0.9
Jordan deaths 37.9 2021-05-03 0.7 0.8 0.9
Bangladesh deaths 70.6 2021-05-03 0.7 0.8 0.9
Hungary deaths 158.1 2021-05-03 0.8 0.8 0.9
Azerbaijan deaths 25.4 2021-05-03 0.7 0.8 0.9

5.3.2 Lowest \(R_{t,m}\) based on cases

Country Estimated Type Daily Count (Last Week) Week Ending R - Lower CI R - Mean R - Uppper CI
Somalia cases 14.7 2021-05-03 0.2 0.2 0.3
Sudan cases 38.7 2021-05-03 0.4 0.5 0.6
Papua New Guinea cases 61.0 2021-05-03 0.5 0.5 0.6
Bosnia and Herzegovina cases 355.7 2021-05-03 0.5 0.6 0.6
Oceania cases 84.6 2021-05-03 0.5 0.6 0.6
Madagascar cases 243.7 2021-05-03 0.6 0.6 0.7
Mali cases 39.9 2021-05-03 0.5 0.6 0.7
Yemen cases 25.7 2021-05-03 0.5 0.6 0.7
Congo cases 48.3 2021-05-03 0.5 0.6 0.8
Ethiopia cases 813.3 2021-05-03 0.6 0.6 0.7

5.3.3 Highest \(R_{t,m}\) based on deaths

Country Estimated Type Daily Count (Last Week) Week Ending R - Lower CI R - Mean R - Uppper CI
Thailand deaths 18.3 2021-05-03 1.6 2.0 2.5
Nepal deaths 26.6 2021-05-03 1.4 1.7 2.0
Costa Rica deaths 17.4 2021-05-03 1.2 1.5 1.7
Cameroon deaths 16.6 2021-05-03 1.0 1.3 1.8
Malaysia deaths 14.6 2021-05-03 1.1 1.3 1.6
Madagascar deaths 10.3 2021-05-03 1.0 1.3 1.6
India deaths 3,502.0 2021-05-03 1.2 1.2 1.3
Japan deaths 60.4 2021-05-03 1.1 1.2 1.3
Egypt deaths 60.6 2021-05-03 1.1 1.2 1.3
Georgia deaths 17.1 2021-05-03 0.9 1.1 1.4

5.3.4 Highest \(R_{t,m}\) based on cases

Country Estimated Type Daily Count (Last Week) Week Ending R - Lower CI R - Mean R - Uppper CI
Benin cases 23.4 2021-05-03 1.6 1.9 2.3
Vietnam cases 19.0 2021-05-03 1.4 1.8 2.3
Cameroon cases 893.1 2021-05-03 1.3 1.8 2.9
Nepal cases 5,693.9 2021-05-03 1.6 1.8 2.0
Sri Lanka cases 1,614.3 2021-05-03 1.5 1.7 1.9
Haiti cases 19.4 2021-05-03 1.3 1.6 2.0
Trinidad and Tobago cases 239.3 2021-05-03 1.4 1.6 1.9
Maldives cases 446.9 2021-05-03 1.4 1.5 1.6
Botswana cases 285.1 2021-05-03 1.2 1.4 1.5
El Salvador cases 179.9 2021-05-03 1.2 1.3 1.4

5.4 Risk Quadrants

The plots below show weekly cases (or deaths) on the X-axis and the reproduction number on the Y-axis. By dividing this into 4 quadrants we can identify countries with high cases and high reproduction numbers, or high cases and low reproduction numbers etc.

Values where the reproduction number exceeds 3 are plotted at 3.

5.4.1 Cases

Risk Quadrants - Cases

5.4.2 Deaths

Risk Quadrants - Deaths

5.5 Country Plots by Continent

Below we plot results for each country/province in a list. Values larger than 3 are plotted at 3.

5.5.1 Africa

5.5.1.1 Algeria

5.5.1.2 Angola

5.5.1.3 Benin

5.5.1.4 Botswana

5.5.1.5 Burkina Faso

5.5.1.6 Burundi

5.5.1.7 Cameroon

5.5.1.8 Cape Verde

5.5.1.9 Central African Republic

5.5.1.10 Chad

5.5.1.11 Comoros

5.5.1.12 Congo

5.5.1.13 Cote d’Ivoire

5.5.1.14 Democratic Republic of Congo

5.5.1.15 Djibouti

5.5.1.16 Egypt

5.5.1.17 Equatorial Guinea

5.5.1.18 Eritrea

5.5.1.19 Eswatini

5.5.1.20 Ethiopia

5.5.1.21 Gabon

5.5.1.22 Gambia

5.5.1.23 Ghana

5.5.1.24 Guinea

5.5.1.25 Guinea-Bissau

5.5.1.26 Kenya

5.5.1.27 Lesotho

5.5.1.28 Liberia

5.5.1.29 Libya

5.5.1.30 Madagascar

5.5.1.31 Malawi

5.5.1.32 Mali

5.5.1.33 Mauritania

5.5.1.34 Mauritius

5.5.1.35 Morocco

5.5.1.36 Mozambique

5.5.1.37 Namibia

5.5.1.38 Niger

5.5.1.39 Nigeria

5.5.1.40 Rwanda

5.5.1.41 Senegal

5.5.1.42 Sierra Leone

5.5.1.43 Somalia

5.5.1.44 South Africa

5.5.1.45 South Sudan

5.5.1.46 Sudan

5.5.1.47 Togo

5.5.1.48 Tunisia

5.5.1.49 Uganda

5.5.1.50 Zambia

5.5.1.51 Zimbabwe

5.5.2 Asia

5.5.2.1 Afghanistan

5.5.2.2 Armenia

5.5.2.3 Azerbaijan

5.5.2.4 Bahrain

5.5.2.5 Bangladesh

5.5.2.6 Bhutan

5.5.2.7 Cambodia

5.5.2.8 China

5.5.2.9 Georgia

5.5.2.10 India

5.5.2.11 Indonesia

5.5.2.12 Iran

5.5.2.13 Iraq

5.5.2.14 Israel

5.5.2.15 Japan

5.5.2.16 Jordan

5.5.2.17 Kazakhstan

5.5.2.18 Kuwait

5.5.2.19 Kyrgyzstan

5.5.2.20 Laos

5.5.2.21 Lebanon

5.5.2.22 Malaysia

5.5.2.23 Maldives

5.5.2.24 Mongolia

5.5.2.25 Myanmar

5.5.2.26 Nepal

5.5.2.27 Oman

5.5.2.28 Pakistan

5.5.2.29 Palestine

5.5.2.30 Philippines

5.5.2.31 Qatar

5.5.2.32 Saudi Arabia

5.5.2.33 Singapore

5.5.2.34 South Korea

5.5.2.35 Sri Lanka

5.5.2.36 Syria

5.5.2.37 Taiwan

5.5.2.38 Thailand

5.5.2.39 Timor

5.5.2.40 Turkey

5.5.2.41 United Arab Emirates

5.5.2.42 Uzbekistan

5.5.2.43 Vietnam

5.5.2.44 Yemen

5.5.3 Europe

5.5.3.1 Albania

5.5.3.2 Austria

5.5.3.3 Belarus

5.5.3.4 Belgium

5.5.3.5 Bosnia and Herzegovina

5.5.3.6 Bulgaria

5.5.3.7 Croatia

5.5.3.8 Cyprus

5.5.3.9 Czechia

5.5.3.10 Denmark

5.5.3.11 Estonia

5.5.3.12 Finland

5.5.3.13 France

5.5.3.14 Germany

5.5.3.15 Greece

5.5.3.16 Hungary

5.5.3.17 Ireland

5.5.3.18 Italy

5.5.3.19 Kosovo

5.5.3.20 Latvia

5.5.3.21 Lithuania

5.5.3.22 Luxembourg

5.5.3.23 Moldova

5.5.3.24 Montenegro

5.5.3.25 Netherlands

5.5.3.26 North Macedonia

5.5.3.27 Norway

5.5.3.28 Poland

5.5.3.29 Portugal

5.5.3.30 Romania

5.5.3.31 Russia

5.5.3.32 Serbia

5.5.3.33 Slovakia

5.5.3.34 Slovenia

5.5.3.35 Spain

5.5.3.36 Sweden

5.5.3.37 Switzerland

5.5.3.38 Ukraine

5.5.3.39 United Kingdom

5.5.4 North America

5.5.4.1 Canada

5.5.4.2 Costa Rica

5.5.4.3 Cuba

5.5.4.4 Dominican Republic

5.5.4.5 El Salvador

5.5.4.6 Guatemala

5.5.4.7 Haiti

5.5.4.8 Honduras

5.5.4.9 Jamaica

5.5.4.10 Mexico

5.5.4.11 Nicaragua

5.5.4.12 Panama

5.5.4.13 Trinidad and Tobago

5.5.4.14 United States

5.5.5 Oceania

5.5.5.1 Australia

5.5.5.2 New Zealand

5.5.5.3 Papua New Guinea

5.5.6 South America

5.5.6.1 Argentina

5.5.6.2 Bolivia

5.5.6.3 Brazil

5.5.6.4 Chile

5.5.6.5 Colombia

5.5.6.6 Ecuador

5.5.6.7 Guyana

5.5.6.8 Paraguay

5.5.6.9 Peru

5.5.6.10 Suriname

5.5.6.11 Uruguay

5.5.6.12 Venezuela

5.6 Detailed Output

Detailed output for all countries are saved to a comma-separated value file. The file can be found here.

6 Discussion

Limitation of this method to estimate \(R_{t,m}\) are noted in [1]

  • It’s sensitive to changes in transmissibility, changes in contact patterns, depletion of the susceptible population and control measures.
  • It relies on an assumed generation interval assumptions.
  • The size of the time window can affect the volatility of results.
  • Results are time lagged with regards to true infection, more so in the case of the use of deaths.
  • It’s sensitive to changes in case (or death) detection.
  • The generation interval may change over time.

Further to the above the estimates are made under assumption that the cases and deaths are reported consistently over time. For cases this means that testing needs to be at similar levels and reported with similar lag. Should these change rapidly over an interval of a few weeks the above estimates of the effective reproduction numbers would be biased. For example a rapid expansion of testing over the last 3 weeks would results in overestimating recent effective reproduction numbers. Similarly any changes in reporting (over time and underreporting) of deaths would also bias estimates of the reproduction number estimated using deaths.

Estimates for the reproduction number are plotted in time period in which the relevant measure is recorded. Though in reality the infections giving rise to those estimates would have occurred roughly between a week to 4 weeks earlier depending on whether it was cases or deaths. These figures have not been shifted back.

Despite these limitation we believe the ease of calculation of this method and the ability to use multiple sources makes it useful as a monitoring tool.

7 Author

This report was prepared by Louis Rossouw. Please get in contact with Louis Rossouw if you have comments or wish to receive this regularly.

Louis Rossouw
Head of Research & Analytics
Gen Re | Life/Health Canada, South Africa, Australia, NZ, UK & Ireland
Email: LRossouw@GenRe.com Mobile: +27 71 355 2550

The views in this document represents that of the author and may not represent those of Gen Re. Also note that given the significant uncertainty involved with the parameters, data and methodology care should be taken with these numbers and any use of these numbers.

References

[1] A. Cori, N. M. Ferguson, C. Fraser, and S. Cauchemez, “A new framework and software to estimate time-varying reproduction numbers during epidemics,” American Journal of Epidemiology, vol. 178, no. 9, pp. 1505–1512, Sep. 2013, doi: 10.1093/aje/kwt133. [Online]. Available: https://doi.org/10.1093/aje/kwt133

[2] A. Cori, EpiEstim: A package to estimate time varying reproduction numbers from epidemic curves. 2013 [Online]. Available: https://CRAN.R-project.org/package=EpiEstim

[3] M. Roser, H. Ritchie, E. Ortiz-Ospina, and J. Hasell, “Coronavirus pandemic (COVID-19),” Our World in Data, 2020 [Online]. Available: https://ourworldindata.org/coronavirus. [Accessed: 17-Dec-2020]